Detection and decoding in flash memories with error correlations for a plurality of bits within a sliding window

ABSTRACT

Methods and apparatus are provided for detection and decoding in flash memories with error correlations for a plurality of bits within a sliding window. Data from a flash memory device is processed by obtaining one or more read values for a plurality of bits from one or more pages of the flash memory device; converting the one or more read values for the plurality of bits to a non-binary log likelihood ratio based on a probability that a given data pattern was written to the plurality of bits when a particular pattern was read from the plurality of bits; and decoding the plurality of bits using a binary decoder. The non-binary log likelihood ratio captures one or more of intra-page correlations and/or intra-cell correlations. A least significant bit and a most significant bit of a given cell can be independently converted and/or jointly converted to the non-binary log likelihood ratio.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation-in-part patent application ofU.S. patent application Ser. No. 12/920,407, filed Jan. 4, 2011,entitled “Methods and Apparatus for Storing Data in a Multi-Level CellFlash Memory Device With Cross-Page Sectors, Multi-Page Coding AndPer-Page Coding;” U.S. patent application Ser. No. 13/063,888, filedAug. 31, 2011, entitled “Methods and Apparatus for Soft Data Generationin Flash Memories;” and U.S. patent application Ser. No. 13/063,895,filed May 31, 2011, entitled “Methods and Apparatus for Soft DataGeneration for Memory Devices Using Reference Cells;” U.S. patentapplication Ser. No. 13/063,899, filed May 31, 2011, entitled “Methodsand Apparatus for Soft Data Generation for Memory Devices Using DecoderPerformance Feedback;” and U.S. patent application Ser. No. 13/063,874,filed Mar. 14, 2011, entitled “Methods and Apparatus for Soft DataGeneration for Memory Devices Based on Performance Factor Adjustment;”and U.S. patent application Ser. No. 13/731,551, filed Dec. 31, 2012,entitled “Multi-Tier Detection and Decoding in Flash Memories,” eachincorporated by reference herein.

FIELD OF THE INVENTION

The present invention relates generally to flash memory devices and moreparticularly, to improved techniques for mitigating the effect of noise,inter-cell interference and other distortions in such flash memorydevices with low overall processing delay.

BACKGROUND OF THE INVENTION

A number of memory devices, such as flash memory devices, use analogmemory cells to store data. Each memory cell stores an analog value,also referred to as a storage value, such as an electrical charge orvoltage. The storage value represents the information stored in thecell. In flash memory devices, for example, each analog memory celltypically stores a certain voltage. The range of possible analog valuesfor each cell is typically divided into threshold regions, with eachregion corresponding to one or more data bit values. Data is written toan analog memory cell by writing a nominal analog value that correspondsto the desired one or more bits.

The analog values stored in memory cells are often distorted. Thedistortions are typically due to, for example, back pattern dependency(BPD), noise and intercell interference (ICI). A number of techniqueshave been proposed or suggested for mitigating the effect of noise. ICI,and other distortions. It is important that such mitigation techniquesdo not unnecessarily impair the write-read speeds for flash readchannels. Thus, many effective signal processing and decoding techniquesare avoided that would introduce inherent processing delays. Foregoingsuch complex signal processing techniques, however, reduces the abilityof the flash control system designer to maintain sufficient decodingaccuracy as flash device geometries scale down.

The smallest writable data unit in a flash memory device is referred toas a page. A page can comprise several codewords of a read channel errorcorrection code (ECC), which is the smallest readable data unit.Depending on the mapping of page bits into memory cell voltages, thereis usually a significant statistical correlation among errors in pagesmapped to the same wordline. Thus, it has been recognized that there arebenefits to coding across multiple pages. In order to maintain highwrite and read speeds in flash memory devices, however, pages aretypically written and decoded on-the-fly, independently from otherpages. A need exists for a decoder design that allows a page to bedecoded on-the-fly in a normal mode of operation, while optionally alsosupporting decoding across multiple pages if a failure occurs, whichtypically rarely happens.

SUMMARY OF THE INVENTION

Generally, methods and apparatus are provided for detection and decodingin flash memories with error correlations for a plurality of bits withina sliding window. According to one aspect of the invention, data from aflash memory device is processed by obtaining one or more read valuesfor a plurality of bits from one or more pages of the flash memorydevice; converting the one or more read values for the plurality of bitsto a non-binary log likelihood ratio based on a probability that a givendata pattern was written to the plurality of bits when a particularpattern was read from the plurality of bits; and decoding the pluralityof bits using a binary decoder.

Generally, the non-binary log likelihood ratio captures one or more ofintra-page correlations and/or intra-cell correlations. The non-binarylog likelihood ratio comprises, for example, a cell-based Galois Fieldvalue that captures intra-cell correlations. In one exemplaryembodiment, a least significant bit and a most significant bit of agiven cell are independently converted to the non-binary log likelihoodratio. In a further variation, a least significant bit and a mostsignificant bit of a given cell are jointly converted to the non-binarylog likelihood ratio.

A more complete understanding of the present invention, as well asfurther features and advantages of the present invention, will beobtained by reference to the following detailed description anddrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic block diagram of an exemplary flash memory systemincorporating detection and decoding techniques in accordance with thepresent invention;

FIG. 2 illustrates an exemplary flash cell array in a multi-level cell(MLC) flash memory device in further detail;

FIG. 3 illustrates the ICI that is present for a target cell due to theparasitic capacitance from a number of exemplary aggressor cells;

FIG. 4 is a schematic block diagram of an exemplary implementation of aflash memory system incorporating detection and decoding techniques inaccordance with aspects of the present invention;

FIG. 5 is a block diagram of an exemplary detection and decoding systemincorporating intra-page correlation aspects of the present invention;

FIG. 6 illustrates an exemplary conventional belief propagation decodingtechnique;

FIG. 7 illustrates an exemplary belief propagation decoding techniqueincorporating aspects of the invention for an exemplary two-bit slidingwindow;

FIG. 8 illustrates an exemplary belief propagation decoding techniqueincorporating aspects of the invention for an exemplary three-bitsliding window;

FIG. 9 illustrates an exemplary bit transition probability table thatrecords collected correlation statistics within a two-bit slidingwindow;

FIG. 10 is a block diagram of an exemplary detection and decoding systemincorporating intra-cell correlation aspects of the present invention;

FIG. 11 illustrates an exemplary belief propagation decoding techniqueincorporating aspects of the invention; and

FIG. 12 illustrates an exemplary transition probability table thatrecords collected intra-wordline statistics indicating a transitionprobability for a given cell value.

DETAILED DESCRIPTION

Various aspects of the invention are directed to signal processingtechniques for mitigating noise, ICI and other distortions in memorydevices, such as single-level cell or multi-level cell (MLC) NAND flashmemory devices. As used herein, a multi-level cell flash memorycomprises a memory where each memory cell stores two or more bits.Typically, the multiple bits stored in one flash cell belong todifferent pages. While the invention is illustrated herein using memorycells that store an analog value as a voltage, the present invention canbe employed with any storage mechanism for flash memories, such as theuse of voltages, currents or resistances to represent stored data, aswould be apparent to a person of ordinary skill in the art.

Aspects of the present invention provide detection and decodingtechniques with error processing that do not unnecessarily impair theread speeds for flash control systems or flash read channels. Accordingto one aspect of the invention, detection and decoding techniques areprovided that account for error correlations between neighboring hitswithin a sliding window. A joint log likelihood ratio for one or morebits in a page is generated in a normal mode based on a probability thata given data pattern was written to one or more bits in the page when aparticular pattern was read. A joint log likelihood ratio is generatedin a recovery mode based on a probability that a given data pattern waswritten to a plurality of bits in a cell when a particular pattern wasread from the plurality of bits. The joint log likelihood ratios for asliding window including a plurality of bits in the same page are basedon statistics collected using past LDPC decisions.

FIG. 1 is a schematic block diagram of an exemplary flash memory system100 incorporating detection and decoding techniques in accordance withaspects of the present invention. As shown in FIG. 1, the exemplaryflash memory system 100 comprises a flash control system 110 and a flashmemory block 160, connected by an interface 150. The exemplary flashcontrol system 110 comprises a flash controller 120, and a read channel125. Moreover, the read channel 125 further comprises an encoder/decoder140, buffers 145 and an LLR generation block 130. Finally, the LLRgeneration block 130 further comprises an ICI mitigation block 135.

As discussed further below in conjunction with FIG. 4, the exemplaryflash controller 120 and read channel 125 implement one or moredetection and decoding processes (discussed further below in conjunctionwith FIGS. 5-12) that incorporate aspects of the present invention.

The exemplary read channel 125 comprises an encoder/decoder block 140and one or more buffers 145. It is noted that the term “read channel”can encompass the write channel as well. In an alternative embodiment,the encoder/decoder block 140 and some buffers 145 may be implementedinside the flash controller 120. The encoder/decoder block 140 andbuffers 145 may be implemented, for example, using well-knowncommercially available techniques and/or products, as modified herein toprovide the features and functions of the present invention.

Generally, as discussed further below in conjunction with FIGS. 4-12,the exemplary LLR generation block 130 processes one or more read valuesfrom the flash memory 160, such as single bit hard values and/orquantized multi-bit soft values, and generates LLR values that areapplied to the decoder 140, such as an exemplary low density paritycheck (LDPC) decoder.

Generally, as discussed further below in conjunction with FIGS. 4-12,the exemplary ICI mitigation block 135 is a specialized function in theLLR generation block 130 that accounts for interference betweenphysically adjacent cells in generating the LLR sequence.

The exemplary flash memory block 160 comprises a memory array 170 andone or more buffers 180 that may each be implemented using well-knowncommercially available techniques and/or products.

In various embodiments of the disclosed detection and decodingtechniques, the exemplary interface 150 may need to convey additionalinformation relative to a conventional flash memory system, such asvalues representing information associated with aggressor cells. Thus,the interface 150 may need to have a higher capacity or faster rate thanan interface in conventional flash memory systems. On the other hand, inother embodiments, this additional information is conveyed to the flashcontroller 120 in a sequential manner which would incur additionaldelays. However those additional delays do not notably increase theoverall delay due to their rare occurrence. When additional capacity isdesired, the interface 150 may optionally be implemented, for example,in accordance with the teachings of International PCT Patent ApplicationSerial No. PCT/US09/4932H, filed Jun. 30, 2009, entitled “Methods andApparatus for Interfacing Between a Flash Memory Controller and a FlashMemory Array”, incorporated by reference herein, which increases theinformation-carrying capacity of the interface 150 using, for example,Double Data Rate (DDR) techniques.

During a write operation, the interface 150 transfers the program valuesto be stored in the target cells, typically using page or wordline levelaccess techniques. For a more detailed discussion of exemplary page orwordline level access techniques, see, for example, International PatentApplication Serial No. PCT/US09/36110, filed Mar. 11, 2009, entitled“Methods and Apparatus for Storing Data in a Multi-Level Cell FlashMemory Device with Cross-Page Sectors, Multi-Page Coding and Per-PageCoding,” incorporated by reference herein.

During a read operation, the interface 150 transfers hard and/or softread values that have been obtained from the memory array 170 for targetand/or aggressor cells. For example, in addition to read values for thepage with the target cell, read values for one or more neighboring pagesin neighboring wordlines or neighboring even or odd bit lines aretransferred over the interface 150. In the embodiment of FIG. 1, thedisclosed detection and decoding techniques are implemented outside theflash memory 160, typically in a process technology optimized for logiccircuits to achieve the lowest area. It is at the expense, however, ofthe additional aggressor cell data that must be transferred on theinterface 150.

FIG. 2 illustrates an exemplary flash cell array 200 in a multi-levelcell (MLC) flash memory device 160 in further detail. As shown in FIG.2, the exemplary flash cell array 200 stores three bits per flash cell,c_(i). FIG. 2 illustrates the flash cell array architecture for oneblock, where each exemplary cell typically corresponds to afloating-gate transistor that stores three bits. The exemplary cellarray 200 comprises in wordlines and n bitlines. Typically, in currentmulti-page cell flash memories, the bits within a single cell belong todifferent pages. In the example of FIG. 2, the three bits for each cellcorrespond to three different pages, and each wordline stores threepages. In the following discussion, pages 0, 1, and 2 are referred to asthe lower, middle, and upper page levels within a wordline.

As indicated above, a flash cell array can be further partitioned intoeven and odd pages, where for example cells with even numbers (such ascells 2 and 4 in FIG. 2) correspond to even pages, and cells with oddnumbers such as cells 1 and 3 in FIG. 2) correspond to odd pages. Inthis case, a page (such as page 0) would contain an even page (even page0) in even cells and an odd page (odd page 0) in odd cells.

In a two-level cell, for example, each cell stores two bits. In oneexemplary implementation, Gray mapping {11, 01, 00, 10} is employedwhere bits in a cell belong to two different pages. The bits for the twopages in each cell are often referred to as the least significant bit(LSB) and the most significant bit (MSB). For example, for the pattern01 that is stored in a two-bit-per-cell flash cell, “1” refers to theLSB or lower page, and “0” refers to the MSB or upper page. Experimentalstudies of flash memory devices indicate that, for example, the errorevent “01”→“10” has considerable occurrence probability at the end ofdevice life. In addition, based on an additive white Gaussian noise(AWGN) model, the MSB page often exhibits a higher bit error rate (BER)compared to the LSB page. Thus, it has been found that in the presenceof such error correlations or error dependencies reading one pageimproves the decoding success probability of the other.

In summary, MSB page and LSB page errors are known to have statisticalcorrelation at the end of life of a flash memory device relative to thebeginning of life. Thus, aspects of the present invention provide jointdecoding of LSB and MSB pages of a given wordline in the recovery mode,while also being able to decode LSB and MSB pages independently in thenormal mode to keep processing delay low.

Intercell Interference

ICI is a consequence of parasitic capacitances between cells and isgenerally considered to be one of the most prominent sources ofdistortion. FIG. 3 illustrates the ICI that is present for a target cell310 due to the parasitic capacitance from a number of exemplaryaggressor cells 320. The following notations are employed in FIG. 3:

WL: wordline;

BL: bitline;

BLo: odd bitline;

BLe: even bitline; and

C: capacitance.

Aspects of the present invention recognize that ICI is caused byaggressor cells 320 that are programmed after the target cell 310 hasbeen programmed. The ICI changes the voltage, V_(t), of the target cell310. In one exemplary embodiment, a “bottom up” programming scheme isassumed and adjacent aggressor cells in wordlines i and i+1 cause ICIfor the target cell 310. With such bottom-up programming of a block, ICIfrom the lower wordline i−1 is removed, and up to five neighboring cellscontribute to ICI as aggressor cells 320, as shown in FIG. 3. It isnoted, however, that the techniques disclosed herein can be generalizedto cases where aggressor cells from other wordlines, such as wordlinei−1, contribute to ICI as well, as would be apparent to a person ofordinary skill in the art. If aggressor cells from wordlines i−1, i andi+1 contribute to ICI, up to eight closest neighboring cells areconsidered. Other cells that are further away from the target cell canbe neglected, if their contribution to ICI is negligible. In general,the aggressor cells 320 are identified by analyzing the programmingsequence scheme (such as bottom up or even/odd techniques) to identifythe aggressor cells 320 that are programmed after a given target cell310.

The ICI caused by the aggressor cells 320 on the target cell 310 can bemodeled in the exemplary embodiment as follows:

$\begin{matrix}{{\Delta\; V_{ICI}^{({i,j})}} = {{k_{x}\Delta\; V_{t}^{({i,{j - 1}})}} + {k_{x}\Delta\; V_{t}^{({i,{j + 1}})}} + {k_{y}\Delta\; V_{t}^{({{i + 1},j})}} + {k_{xy}\Delta\; V_{t}^{({{i + 1},{j - 1}})}} + {k_{xy}\Delta\; V_{t}^{({{i + 1},{j + 1}})}}}} & (1)\end{matrix}$where ΔV_(t) ^((w,b)) is the change in V_(t) voltage of agressor cell(w,b),

Δ V_(ICI)^((i, j))is the change in V_(t) voltage of target cell (i,j) due to ICI andk_(x), k_(y) and k_(xy) are capacitive coupling coefficients for the x,y and xy direction.

Generally, V_(t) is the voltage representing the data stored in a celland obtained during a read operation. V_(t) can be obtained by a readoperation, for example, as a soft voltage value with more precision thanthe number of bits stored per cell when all pages in a wordline areread, or with two or more bits when only one page in a wordline is read,or as a value quantized to a hard voltage level with the same resolutionas the number of bits stored per cell (e.g., 3 bits for 3 bits/cellflash) when all pages in a wordline are read, or a value quantized toone hard bit when only one page in a wordline is read.

For a more detailed discussion of distortion in flash memory devices,see, for example, J. D. Lee et al., “Effects of Floating-GateInterference on NAND Flash Memory Cell Operation,” IEEE Electron DeviceLetters, 264-266 (May 2002) or Ki-Tae Park, et al., “A ZeroingCell-to-Cell Interference Page Architecture With Temporary LSB Storingand Parallel MSB Program Scheme for MLC NAND Flash Memories,” IEEE J. ofSolid State Circuits, Vol. 43, No. 4, 919-928, (April 2008), eachincorporated by reference herein.

For a more detailed discussion of ICI mitigation, see, for example,International Patent Application Serial No. PCT/US09/49326, filed Jun.30, 2009, entitled “Methods and Apparatus for Read-Side IntercellInterference Mitigation in Flash Memories,” incorporated by referenceherein.

FIG. 4 is a schematic block diagram of an exemplary implementation of aflash memory system 400 incorporating detection and decoding techniquesin accordance with aspects of the present invention. As shown in FIG. 4,one or more read values are obtained from the memory array 170 of theflash memory 160. The read values may be, for example, a hard value or asoft value. In a normal mode, for example, a read value is obtained forat least one bit in a given page.

In a given processing mode, such as a normal mode or a recovery mode, anexemplary LLR generation block 420 processes the read values from theflash memory 160, such as single bit hard values and/or quantizedmulti-bit soft values, and generates LLR values that are applied to anexemplary LDPC decoder 430. The LLR generation performed by theexemplary LLR generation block 420 for each mode of the exemplarydetection and decoding is discussed further below in a section entitled“LLR generation.”

An exemplary flash controller 425 implements one or more detection anddecoding processes (discussed further below in conjunction with FIGS.5-12) that incorporate aspects of the present invention. In addition, asdiscussed further below, an exemplary LDPC decoder 430 processes theLLRs generated by the exemplary LLR generation block 420 and provideshard decisions that are stored in hard decision buffers 440.

As discussed further below, the exemplary LDPC decoder 430 caniteratively decode the LLR values, e.g., until the read values aresuccessfully decoded. Iterations inside the LDPC decoder 430 are calledlocal iterations. In these local iterations, LLRs are being updatedinside the LDPC decoder using one or more iterations of a messagepassing algorithm. In addition, as discussed further below, in anexemplary recovery mode, the exemplary LLR generation block 420 and theexemplary LDPC decoder 430 can globally iterate until the read valuesare successfully decoded. In a global iteration, the LLR generationblock 420 provides LLRs to the LDPC decoder 430. After local iterationswithin the LDPC decoder, the LDPC decoder then provides updated LLRs tothe LLR generation block. The LLR generation block uses these LLRs fromthe LDPC decoder to compute updated LLRs, which are provided to the LDPCdecoder. One loop of LLR updates through the LLR generation block andLDPC decoder is called one global iteration. In an iterative detectionand decoding system, several local and/or several global iterations arebeing performed until the data corresponding to a codeword has beensuccessfully detected and decoded. For a more detailed discussion ofiterative detection and decoding using local and global iterations, see,for example, U.S. patent application Ser. No. 13/063,888, filed Mar. 14,2011, entitled “Methods and Apparatus for Soft Data Generation in FlashMemories,” incorporated by reference herein.

FIG. 5 is a block diagram of an exemplary detection and decoding system500 incorporating intra-page correlation aspects of the presentinvention. As discussed hereinafter, the exemplary detection anddecoding system 500 performs on-the-fly decoding of individual pages ina normal mode for an exemplary two-level cell flash memory 160.

As shown in FIG. 5, the exemplary detection and decoding system 500processes LSB and MSB page hard data 510-1, 510-2. Each page of the LSBand MSB page hard data 510-1, 510-2 is separately processed by an LSBpage sliding window error statistics collection block 520-1 and an MSBpage sliding window error statistics collection block 520-2 to collectthe error statistics within a sliding window that are used for LLRgeneration. The error statistics collected within the sliding window arestored in a corresponding table 900, as discussed further below inconjunction with FIG. 9.

In a normal mode, the LSB and MSB page hard data 510-1, 510-2 areprocessed independently. A first sliding window LLR generator 530-1processes the LSB hard values 510-1 and a second sliding window LLRgenerator 530-2 processes the MSB hard values 510-2. The sliding windowLLR generators 530-1, 530-2 generate corresponding LLRs. In an exemplaryembodiment, the LSB and MSB page hard data 510-1, 510-2 can be processedusing a sliding window of 2 or 3 bits and the corresponding LLRscomprise 4 or 8 LLRs per bit.

The LLRs for the LSB and MSB pages are applied to a corresponding LSBbinary LDPC decoder 540-1 that generates the recovered LSB page or thecorresponding MSB binary LDPC decoder 540-2 that generates the recoveredMSB page. The LSB and MSB binary LDPC decoders 540 each optionallyperform local iterations 550.

Global iterations between the channel and decoder functions are notneeded, as the modified belief propagation decoding algorithm in theLDPC decoders typically captures a sufficient amount of correlationbetween decoded bits to sustain the least delay in this on-the-flydecoding mode.

FIG. 6 illustrates an exemplary conventional belief propagation decodingtechnique 600. For a detailed discussion of a suitable exemplaryconventional belief propagation decoding technique, see, for example, WuChang and J. R. Cruz, “An Improved Belief-Propagation Decoder forLDPC-Coded Partial-Response Channels,” IEEE Trans. on Magnetics, Vol.46, No. 7, 2639-648 (July 2010), incorporated by reference herein.

As shown in FIG. 6, messages are exchanged between check nodes 610 andbit nodes 620, in a known manner. Messages u, such as message u(c₁^(i)→b_(i)), are sent from check nodes 610 to bit nodes 620 and messagesv, such as message v(b_(i)→c₃ ^(i)), are sent from bit nodes 620 tocheck nodes 610. It is noted that in the embodiment of FIG. 6, the bitnodes 620 do not exchange messages directly with one another, thoughinformation can be relayed indirectly by a check node connected to bothbit nodes.

In addition, independent channel LLRs 630 (λ(b)) are generated andapplied to the bit nodes 620, in a known manner.

A quantity U is a sum of all messages u, such as message u(c₁^(i)→b_(i)), sent from all connected check nodes 610 to a particular bitnode 620. U can be computed for bit nodes b_(i) and b_(i+1) as follows:

${U\left( b_{i} \right)} = {\sum\limits_{j = 1}^{3}{u\left( c_{j}^{i}\rightarrow b_{i} \right)}}$${U\left( b_{i + 1} \right)} = {\sum\limits_{j = 1}^{3}{u\left( c_{j}^{i + 1}\rightarrow b_{i + 1} \right)}}$

In addition, an intermediate quantity V comprises the quantity U as wellas any messages received by a given bit node b_(i) from the channel,such as the independent channel LLRs 630 (λ(b_(i))). V can be computedfor a given bit node b_(i) as follows:V(b _(i))=U(b _(i))+λ(b _(i))

Finally, once V is computed, the messages from a given bit node 620 to aparticular check node 610 can be expressed as follows:v(b _(i) →c _(j) ^(i))=V(b _(i))−u(c _(j) ^(i) →b _(i)).

Generally, the messages from a given bit node 620 to a particular checknode 610 comprise the quantity V for the given bit node b_(i) less themessage from the connected particular check node 610 to the given bitnode 620 received in the previous LDPC decoder iteration. On the otherhand, the way these v messages are processed at the check nodes togenerate the new u messages in the next iteration is similar to anyconventional belief propagation decoding technique used in the priorart.

FIG. 7 illustrates an exemplary belief propagation decoding technique700 incorporating aspects of the invention for an exemplary two-bitsliding window. For a more general description, see for example, WuChang and J. R. Cruz, “An Improved Belief-Propagation Decoder forLDPC-Coded Partial-Response Channels,” IEEE Trans. on Magnetics, Vol.46, No. 7, 2639-648 (July 2010), incorporated by reference herein.

As shown in FIG. 7, messages are exchanged between check nodes 710 andbit nodes 720, in a similar manner to FIG. 6. Messages u, such asmessage u(c₁ ^(i)→b_(i)), are sent from check nodes 710 to bit nodes 720and messages v, such as message v(b_(i)→c₃ ^(i)), are sent from bitnodes 720 to check nodes 710. In addition, as discussed hereinafter, inthe embodiment of FIG. 7, the bit nodes 720 exchange messages U with oneanother, where messages U in FIG. 7 are calculated using the sameformulas as those of FIG. 6. In addition, as discussed hereinafter,sliding window-based 2-bit LLRs 730 (λ(b_(i)b_(i+1))) are generated andapplied to the bit nodes 720.

Finally, the main difference between the exemplary embodiment of FIG. 7and the conventional embodiment of FIG. 6 is the calculation ofintermediate quantity V, which is a function of the messages U exchangedamong the bit nodes 720 and any messages received by a given set of bitsb_(i)b_(i+1) from the channel, such as the sliding window-based 2-bitLLRs 730 (λ(b_(i)b_(i+1))) for each possible value combination of bits bV can be computed for a given bit b_(i) as follows:

V(b_(i)) = max^(*)[λ(b_(i)b_(i + 1) = 00) + U(b_(i + 1)), λ(b_(i)b_(i + 1) = 01)] − max^(*)[λ(b_(i)b_(i + 1) = 10) + U(b_(i + 1)), λ(b_(i)b_(i + 1) = 11)] + U(b_(i)).where max*(ψ,ζ)=max(ψ, ζ)+log(1+e^(−|ψ−ζ|)). Then, once V is computed,the messages from a given bit node 720 to a particular check node 710can be expressed in the same way as FIG. 6.

FIG. 8 illustrates an exemplary belief propagation decoding technique800 incorporating aspects of the invention for an exemplary three-bitsliding window. As shown in FIG. 8, messages are exchanged between checknodes 810 and bit nodes 820, in a similar manner to FIG. 7. Messages u,such as message u(c₁ ^(i)→b_(i)), are sent from check nodes 810 to bitnodes 820 and messages v, such as message v(b_(i)→c₃ ^(i)), are sentfrom bit nodes 820 to check nodes 810. In addition, as discussedhereinafter, in the embodiment of FIG. 8, the bit nodes 820 exchangemessages U with one another, and messages U in FIG. 8 are calculatedusing the same formulas as those of FIG. 6. In addition, as discussedhereinafter, sliding window-based 3-bit LLRs 830(λ(b_(i)b_(i+1)b_(i+2))) are generated and applied to the bit nodes 820.

In addition, a quantity V is a function of the messages U exchangedamong the bit nodes 820 and any messages received by a given set of bitsb_(i)b_(i+1)b_(i+2) from the channel, such as the sliding window-based3-bit LLRs 830 (λ(b_(i)b_(i+1)b_(i+2))) for each possible combination ofbits b_(i)b_(i+1)b_(i+2). V can be computed for a given bit b_(i) asfollows:

${V\left( b_{i} \right)} = {{\max^{*}\begin{bmatrix}{{{\lambda\left( {{b_{i}b_{i + 1}b_{i + 2}} = 000} \right)} + {U\left( b_{i + 1} \right)} + {U\left( b_{i + 2} \right)}},} \\{{{\lambda\left( {{b_{i}b_{i + 1}b_{i + 2}} = 001} \right)} + {U\left( b_{i + 1} \right)}},} \\{{{\lambda\left( {{b_{i}b_{i + 1}b_{i + 2}} = 010} \right)} + {U\left( b_{i + 2} \right)}},} \\{\lambda\left( {{b_{i}b_{i + 1}b_{i + 2}} = 011} \right)}\end{bmatrix}} - {\max^{*}\begin{bmatrix}{{{\lambda\left( {{b_{i}b_{i + 1}b_{i + 2}} = 100} \right)} + {U\left( b_{i + 1} \right)} + {U\left( b_{i + 2} \right)}},} \\{{{\lambda\left( {{b_{i}b_{i + 1}b_{i + 2}} = 101} \right)} + {U\left( b_{i + 1} \right)}},} \\{{{\lambda\left( {{b_{i}b_{i + 1}b_{i + 2}} = 110} \right)} + {U\left( b_{i + 2} \right)}},} \\{\lambda\left( {{b_{i}b_{i + 1}b_{i + 2}} = 111} \right)}\end{bmatrix}} + {{U\left( b_{i} \right)}.}}$

FIG. 9 illustrates an exemplary bit transition probability table 900that records collected correlation statistics within a two-bit slidingwindow indicating a transition probability for a given pair of bits(b_(i)b_(i+1)) on the same page. Generally, for a two-bit slidingwindow, two bits (b_(i)b_(i+1)) are first considered, followed by twobits (b_(i+1)b_(i+2)) and so on for each considered bit.

The size of the transition probability table 900 grows exponentially inthe number of considered neighboring bits, and the error statistics inthe exemplary bit transition probability table 900 are used to compute2-bit joint LLRs within the sliding window, where the LLRs are definedon a Galois Field of dimension 4 (GF(4)).

In the exemplary embodiment, the 2-bit LLRs arc calculated based onerror statistics of the adjacent bits in the same wordline. The errorstatistics can be collected using reference cells or past LDPC decisionsof the pages in the wordline. For a discussion of suitable referencecell techniques, see, for example, U.S. patent application Ser. No.13/063,899, filed Mar. 14, 2011, entitled “Methods and Apparatus forSoft Data Generation for Memory Devices Using Decoder PerformanceFeedback;” and/or U.S. patent application Ser. No. 13/063,895, filedMar. 14, 2011, entitled “Methods and Apparatus for Soft Data Generationfor Memory Devices Using Reference Cells,” each incorporated byreference herein. The transition probability table 900 records aprobability that each possible pattern was written to bits(b_(i)b_(i+1)) given that each possible pattern was read (i.e., thereliability of making a decision that a given pattern was written giventhat a given pattern was read in the normal mode 500). For example, theterm “p(10/00)” indicates the probability that the pattern ‘10’ waswritten to bits (b_(i)b_(i+1)) given that pattern ‘00’ was read for bits(b_(i)b_(i+1)) (or the reliability of making a decision ‘10’ given ‘00’was read in the normal mode).

The correlation sliding window center can be a function of the bitcorrelation profile, where for a symmetric correlation profile thecenter should match the spatial center of the sliding window. In someinstances if the length of the sliding window is even, then the centeris chosen to best match the correlation profile and several options canbe equivalent. The correlation profile can be determined based oncharacterization of the flash provided by a particular flash vendor. Inone example, in the formula above pertaining to FIG. 8, among all threebits the center is the bit with index i among indices i, i+1, and i+2.This means that a bit is mostly correlated with the next two bits andnot the old bits. But in flash applications, due to intra page ICI, abit will typically be correlated equally with the right and left bits sothe sliding window center would be chosen as i+1.

The statistics in the transition probability table 900 can be employedto compute 2-bit LLRs 730 as follows. Given that a particular patternwas read, such as a pattern of ‘00’, the corresponding LLRs can becomputed using distribution marginalization as follows:λ(b _(i) b _(i+1)=00|00)=log[p(00|00)]−C, λ(b _(i) b_(i+1)=01|00)=log[p(01/00)]·Cλ(b _(i) b _(i+1)=10|00)=log[p(10/00)]−C, λ(b _(i) b_(i+1)=11|00)=log[p(11/00)]−Cwhere C is a normalization constant.

In a further variation, the bit transition probability table 900 can bea function of one or more performance factors, such as endurance, numberof program/erase cycles, number of read cycles, retention time,temperature, temperature changes, process corner, ICI impact, locationwithin the memory array 170, location of wordline and/or page from whichthe read values are obtained, location of page within wordline fromwhich the read values are obtained and a pattern of aggressor cells. Oneor more of the performance factors can be varied for one or moredifferent bits within a cell, different pages within a wordline,different bit lines or different hard read data values. For a moredetailed discussion of suitable techniques for computing a loglikelihood ratio for memory devices based on such performance factoradjustments, see, for example, International Patent Application SerialNo. PCT/US09/59069, filed Sep. 30, 2009, entitled “Methods and Apparatusfor Soft Data Generation for Memory Devices Based on Performance FactorAdjustment,” incorporated by reference herein.

FIG. 10 is a block diagram of an exemplary detection and decoding system1000 incorporating intra-cell correlation aspects of the presentinvention. As discussed hereinafter, the exemplary detection anddecoding system 1000 performs decoding in a recovery mode for anexemplary two-level cell flash memory 160 using joint LLRs. Theexemplary detection and decoding system 1000 processes LSB and MSB pagehard data 1010-1, 1010-2 in a recovery mode. The bits of the LSB and MSBpage hard data 1010-1, 1010-2 are applied to bit-to-symbol (B/S)converters 1020, 1040 that map the two LSB and MSB bits to symbols. Forexample, under a Gray encoding scheme, the following exemplary mappingof 2-bits to a symbol can be employed:

00→0; 01→1; 10→2; and 11→3.

The symbols are processed along a first path by a GF(4) cell-based errorstatistics collection block 1030 that collects the cell-based errorstatistics that are used for LLR generation, as discussed further below.The collected error statistics are stored in a corresponding table 1200,as discussed further below in conjunction with FIG. 12.

Likewise, the joint LSB/MSB symbols are processed along a second path bya GF(4) LLR generator 1050. The GF(4) LLR generator 1050 generatescorresponding symbol (joint) LLRs. In an exemplary embodiment, thecorresponding GF(4) LLRs comprise 4 LLRs per bit.

The GF(4) LLRs are applied to a corresponding LSB LDPC decoder 1060-1that generates the recovered LSB page or the corresponding MSB LDPCdecoder 1060-2 that generates the recovered MSB page. The LSB and MSBLDPC decoders 1060 optionally perform local iterations 1070 betweenthem.

FIG. 11 illustrates an exemplary belief propagation decoding technique1100 incorporating aspects of the invention. As shown in FIG. 11, in arecovery mode, LSB and MSB check nodes 1110-L. 1110-M are processedseparately and LSB and MSB bit nodes 1120-L, 1120-M are processedseparately. Messages are exchanged from LSB check nodes 1110-L to LSBbit nodes 1120-L, and separately from MSB check nodes 1110-M to MSB bitnodes 1120-M. In addition, messages are exchanged between LSB and MSBbit nodes 1120-L, 1120-M.

In addition, joint 2-bit cell-based LLRs 1130 are applied to both LSBand MSB bit nodes 1120-L, 1120-M.

In addition, a quantity V for a given MSB bit node b_(i) is a functionof the messages U exchanged from the corresponding LSB bit node a_(i)and any messages received by a given set of bits b_(i)a_(i) from thechannel, such as the cell-based 2-bit LLRs 1130 (λ(b_(i)a_(i))) for eachpossible combination of the values of bits b_(i)a_(i). V can be computedfor a given bit b_(i) as follows:

V(b_(i)) = max^(*)[λ(b_(i)a_(i) = 00) + U(a_(i)), λ(b_(i)a_(i) = 01)] − max^(*)[λ(b_(i)a_(i) = 10) + U(a_(i)), λ(b_(i)a_(i) = 11)] + U(b_(i)).

FIG. 12 illustrates an exemplary transition probability table 1200 thatrecords collected intra-wordline statistics indicating a transitionprobability for a given cell value conditioned on neighboring bits in awordline (e.g., adjacent bits in the same cell). The size of thetransition probability table 1200 grows exponentially in the number ofconsidered neighboring bits.

In the exemplary embodiment, the LLRs are calculated based on errorstatistics of the adjacent bits in the same cell. The error statisticscan be collected using reference cells or past LDPC decisions of thepages in the wordline. For a discussion of suitable reference celltechniques, see, for example, U.S. patent application Ser. No.13/063,899, filed Mar. 14, 2011, entitled “Methods and Apparatus forSoft Data Generation for Memory Devices Using Decoder PerformanceFeedback;” and/or U.S. patent application Ser. No. 13/063,895, filedMar. 14, 2011, entitled “Methods and Apparatus for Soft Data Generationfor Memory Devices Using Reference Cells,” each incorporated byreference herein. The transition probability table 1200 records aprobability that each possible pattern was written to cell i given thateach possible pattern was read (i.e., the reliability of making adecision that a given pattern was written given that a given pattern wasread in the normal mode 500). For example, the term “p(10/00)” indicatesthe probability that the pattern ‘10’ was written to bits a_(i)b_(i) incell i given that pattern ‘00’ was read for bits a_(i)b_(i) (or thereliability of making a decision ‘10’ given ‘00’ was read in the normalmode).

The statistics in the transition probability table 1200 can be employedto compute joint LLRs as follows. Given that a particular pattern wasread, such as a pattern of ‘00’, the corresponding LLRs can be computedusing distribution marginalization as follows:λ(a _(i) b ₁=00|00)=log[p(00/00)]−C, λ(a _(i) b_(i)=01|00)=log[p(01/00)]−Cλ(a _(i) b _(i)=10|00)=log[p(10/00)]−C, λ(a _(i) b_(i)=11|00)=log[p(11/00)]−Cwhere C is a normalization constant.

In a further variation, the transition probability table 1200 can be afunction of one or more performance factors, including wordline index,as discussed above in conjunction with FIG. 9.

Non-Binary LLR Generation for Recovery Mode

As indicated above, FIG. 12 illustrates an exemplary transitionprobability table 1200 that records collected intra-wordline statisticsindicating a transition probability for a given cell value. The size ofthe transition probability table 1200 grows exponentially in the numberof pages in a wordline, or in one embodiment it grows exponentially inthe total number of considered neighboring bits. The error statistics inthe exemplary bit transition probability table 1200 are used to compute2-bit joint GF(4) LLRs that correspond to the 4 possible states of aflash cell. For more information on bit transition probability tables,see U.S. patent application Ser. No. 13/731,766, filed Dec. 31, 2012,entitled “Detection and Decoding in Flash Memories Using Correlation ofNeighboring Bits,” incorporated by reference herein. An exemplarynon-binary recovery mode detection and decoding process uses wordline(cell) access techniques, where the other pages in the wordline are readto generate the corresponding LLRs. In the exemplary embodiment, theLLRs are calculated based on data or error statistics of the adjacentbits in the same wordline, or based on data or error statistics of otheraggressor wordlines that are being considered. The data or errorstatistics can be collected using reference cells or past LDPC decisionsof the pages in the wordline. For a discussion of suitable errorstatistics collection techniques, see, for example, U.S. patentapplication Ser. No. 13/063,895, filed Mar. 14, 2011, entitled “Methodsand Apparatus for Soft Data Generation for Memory Devices UsingReference Cells;” and/or U.S. patent application Ser. No. 13/063,899,filed Mar. 14, 2011, entitled “Methods and Apparatus for Soft DataGeneration for Memory Devices Using Decoder Performance Feedback,” eachincorporated by reference herein.

The transition probability table 1200 records a transition probabilityfor a given cell value conditioned on neighboring bits in a wordline(e.g., adjacent bits in the same cell). The size of the transitionprobability table 1200 grows exponentially in the number of consideredneighboring bits. For example, the term “p(10/00)” indicates theprobability that the pattern ‘10’ was written to bits a_(i)b_(i) giventhat pattern ‘00’ was read (or the reliability of making a decision ‘10’given ‘00’ was read in the normal mode). This table can also be used forbits in other cells, such as cell i+1 as would be apparent to a personof ordinary skill in the art. It is again noted that both pages in thewordline are read in a non-binary recovery mode.

The statistics in the transition probability table 1200 can be employedto compute LLRs as follows. Given that a particular pattern was read,such as a pattern of ‘00’, the corresponding symbol LLRs can be computedas, where C is some normalization constant,)λ(a _(i) b _(i)=00|00)=log[p(00/00)]−C, λ(a _(i) b_(i)=01|00)=log[p(01/00)]−C;λ(a _(i) b ₁=10|00)=log[p(10/00)]−C, λ(a _(i) b_(i)=11|00)=log[p(11/00)]−C

For a discussion of LLR generation conditioned on several designatedneighboring bits, see. U.S. patent application Ser. No. 13/731,766,filed Dec. 31, 2012, entitled “Detection and Decoding in Flash MemoriesUsing Correlation of Neighboring Bits,” incorporated by referenceherein.

In a further variation, the bit transition probability table 1200 can bea function of one or more performance factors, such as endurance, numberof program/erase cycles, number of read cycles, retention time,temperature, temperature changes, process corner, ICI impact, locationwithin the memory array 170, location of wordline and/or page from whichthe read values are obtained, location of page within wordline fromwhich the read values are obtained and a pattern of aggressor cells. Oneor more of the performance factors can be varied for one or moredifferent bits within a cell, different pages within a wordline,different bit lines or different hard read data values. For a moredetailed discussion of suitable techniques for computing a loglikelihood ratio for memory devices based on such performance factoradjustments, see, for example, International Patent Application SerialNo. PCT/US09/59069, filed Sep. 30, 2009, entitled “Methods and Apparatusfor Soft Data Generation for Memory Devices Based on Performance FactorAdjustment,” incorporated by reference herein.

Process, System and Article of Manufacture Details

While a number of flow charts herein describe an exemplary sequence ofsteps, it is also an embodiment of the present invention that thesequence may be varied. Various permutations of the algorithm arecontemplated as alternate embodiments of the invention. While exemplaryembodiments of the present invention have been described with respect toprocessing steps in a software program, as would be apparent to oneskilled in the art, various functions may be implemented in the digitaldomain as processing steps in a software program, in hardware by circuitelements or state machines, or in combination of both software andhardware. Such software may be employed in, for example, a digitalsignal processor, application specific integrated circuit,micro-controller, or general-purpose computer. Such hardware andsoftware may be embodied within circuits implemented within anintegrated circuit.

Thus, the functions of the present invention can be embodied in the formof methods and apparatuses for practicing those methods. One or moreaspects of the present invention can be embodied in the form of programcode, for example, whether stored in a storage medium, loaded intoand/or executed by a machine, or transmitted over some transmissionmedium, wherein, when the program code is loaded into and executed by amachine, such as a computer, the machine becomes an apparatus forpracticing the invention. When implemented on a general-purposeprocessor, the program code segments combine with the processor toprovide a device that operates analogously to specific logic circuits.The invention can also be implemented in one or more of an integratedcircuit, a digital signal processor, a microprocessor, and amicro-controller.

As is known in the art, the methods and apparatus discussed herein maybe distributed as an article of manufacture that itself comprises acomputer readable medium having computer readable code means embodiedthereon. The computer readable program code means is operable, inconjunction with a computer system, to carry out all or some of thesteps to perform the methods or create the apparatuses discussed herein.The computer readable medium may be a tangible recordable medium (e.g.,floppy disks, hard drives, compact disks, memory cards, semiconductordevices, chips, application specific integrated circuits (ASICs)) or maybe a transmission medium (e.g., a network comprising fiber-optics, theworld-wide web, cables, or a wireless channel using time-divisionmultiple access, code-division multiple access, or other radio-frequencychannel). Any medium known or developed that can store informationsuitable for use with a computer system may be used. Thecomputer-readable code means is any mechanism for allowing a computer toread instructions and data, such as magnetic variations on a magneticmedia or height variations on the surface of a compact disk.

The computer systems and servers described herein each contain a memorythat will configure associated processors to implement the methods,steps, and functions disclosed herein. The memories could be distributedor local and the processors could be distributed or singular. Thememories could be implemented as an electrical, magnetic or opticalmemory, or any combination of these or other types of storage devices.Moreover, the term “memory” should be construed broadly enough toencompass any information able to be read from or written to an addressin the addressable space accessed by an associated processor. With thisdefinition, information on a network is still within a memory becausethe associated processor can retrieve the information from the network.

It is to be understood that the embodiments and variations shown anddescribed herein are merely illustrative of the principles of thisinvention and that various modifications may be implemented by thoseskilled in the art without departing from the scope and spirit of theinvention.

We claim:
 1. A method for processing data from a flash memory device,comprising: obtaining one or more read values for a plurality of bitsfrom one or more pages of said flash memory device; converting said oneor more read values for said plurality of bits to a non-binary loglikelihood ratio based on a probability that a given data pattern waswritten to said plurality of bits when a particular pattern was readfrom said plurality of bits; and decoding said plurality of bits using abinary decoder.
 2. The method of claim 1, wherein said binary decodercomprises one or more binary Low Parity Density Check Message Passingdecoders.
 3. The method of claim 1, wherein said non-binary loglikelihood ratio captures one or more of intra-cell correlations andintra-page correlations.
 4. The method of claim 1, wherein a leastsignificant bit and a most significant bit of a given cell areindependently converted to said non-binary log likelihood ratio.
 5. Themethod of claim 4, wherein said decoding of said non-binary loglikelihood ratio for said least significant bit and said non-binary loglikelihood ratio for said most significant bit of said given cell areperformed independently.
 6. The method of claim 5, wherein said localiterations for said decoding of said least significant bit and said mostsignificant bit of said given cell are performed independently.
 7. Themethod of claim 4, wherein said decoding of said least significant hitand said decoding of said most significant hit are performed usingindependent message passing graphs.
 8. The method of claim 4, whereinsaid non-binary log likelihood ratios for said least significant bit andsaid most significant bit are based on error statistics collected in asliding window for a given page.
 9. The method of claim 4, wherein saiddecoding further comprises the step of computing a sum V of the messagesinto a given bit node b_(i) as a function of messages U exchanged amongbit nodes and messages λ(b_(i)b_(i+1)) received by a given set of bitsb_(i)b_(i+1) for each possible combination of bits b_(i)b_(i+1), asfollows:V(b_(i)) = max^(*)[λ(b_(i)b_(i + 1) = 00) + U(b_(i + 1)), λ(b_(i)b_(i + 1) = 01)] − max^(*)[λ(b_(i)b_(i + 1) = 10) + U(b_(i + 1)), λ(b_(i)b_(i + 1) = 11)] + U(b_(i)).where max*(ψ, ζ)=max(ψ, ζ)+log(1+e^(−|ψ−ζ|)).
 10. The method of claim 4,wherein said decoding further comprises the step of computing a sum V ofthe messages into a given bit node b_(i) as a function of messages Uexchanged among bit nodes and messages λ(b_(i)b_(i−1)b_(i+2)) receivedby a given set of bits b_(i)b_(i+1)b_(i+2) for each possible combinationof bits b_(i)b_(i+1)b_(i+2), as follows:${V\left( b_{i} \right)} = {{\max^{*}\begin{bmatrix}{{{\lambda\left( {{b_{i}b_{i + 1}b_{i + 2}} = 000} \right)} + {U\left( b_{i + 1} \right)} + {U\left( b_{i + 2} \right)}},} \\{{{\lambda\left( {{b_{i}b_{i + 1}b_{i + 2}} = 001} \right)} + {U\left( b_{i + 1} \right)}},} \\{{{\lambda\left( {{b_{i}b_{i + 1}b_{i + 2}} = 010} \right)} + {U\left( b_{i + 2} \right)}},} \\{\lambda\left( {{b_{i}b_{i + 1}b_{i + 2}} = 011} \right)}\end{bmatrix}} - {\max^{*}\begin{bmatrix}{{{\lambda\left( {{b_{i}b_{i + 1}b_{i + 2}} = 100} \right)} + {U\left( b_{i + 1} \right)} + {U\left( b_{i + 2} \right)}},} \\{{{\lambda\left( {{b_{i}b_{i + 1}b_{i + 2}} = 101} \right)} + {U\left( b_{i + 1} \right)}},} \\{{{\lambda\left( {{b_{i}b_{i + 1}b_{i + 2}} = 110} \right)} + {U\left( b_{i + 2} \right)}},} \\{\lambda\left( {{b_{i}b_{i + 1}b_{i + 2}} = 111} \right)}\end{bmatrix}} + {{U\left( b_{i} \right)}.}}$ where max*(ψ, ζ)=max(ψ,ζ)+log(1+e^(−|ψ−ζ|)).
 11. The method of claim 1, wherein said non-binarylog likelihood ratio comprises a cell-based Galois Field value thatcaptures intra-cell correlations.
 12. The method of claim 1, wherein aleast significant bit and a most significant bit of a given cell arejointly converted to said non-binary log likelihood ratio.
 13. Themethod of claim 12, wherein said decoding of said joint non-binary loglikelihood ratio for said least significant bit and said mostsignificant bit of said given cell are performed independently.
 14. Themethod of claim 12, wherein said decoding of said joint non-binary loglikelihood ratio for said least significant bit and said mostsignificant bit of said given cell are performed independently withjoint local iterations.
 15. The method of claim 12, wherein saiddecoding of said least significant bit and said decoding of said mostsignificant bit are performed using independent message passing graphsthat communicate with one another.
 16. The method of claim 12, whereinsaid decoding further comprises the step of computing a sum V ofmessages into a given bit node b_(i) as a function of messages Uexchanged from a corresponding least significant bit node a_(i) and anymessages λ(b_(i)a_(i)) received by a given set of bits b_(i)a_(i) in acell for each possible combination of bits b_(i)a_(i), follows:V(b_(i)) = max^(*)[λ(b_(i)a_(i) = 00) + U(a_(i)), λ(b_(i)a_(i) = 01)] − max^(*)[λ(b_(i)a_(i) = 10) + U(a_(i)), λ(b_(i)a_(i) = 11)] + U(b_(i)).where max*(ψ, ζ)=max(ψ, ζ)+log(1+e^(−|ψ−ζ|)).
 17. The method of claim 1,wherein said method comprises a recovery mode that is initiated if anormal operating mode does not successfully decode one or more of saidplurality of bits for a given page.
 18. The method of claim 1, whereinsaid probability that said given data pattern was written to saidplurality of bits when said particular pattern was read from saidplurality of bits is obtained from one or more tables.
 19. The method ofclaim 1, wherein said given data pattern comprises one or more of aplurality of bits in a given page and a plurality of bits in a givencell.
 20. The method of claim 1, wherein said probability that saidgiven data pattern was written to said plurality of bits when saidparticular pattern was read from said plurality of bits is based on oneor more of one or more reference cells, one or more prior decodeddecisions and one or more performance factors of said flash memorydevice.
 21. A tangible machine-readable recordable storage medium forprocessing data from a flash memory device, wherein one or more softwareprograms when executed by one or more processing devices implement thesteps of the method of claim
 1. 22. A flash memory system, comprising: areliability unit for converting one or more read values for a pluralityof bits from one or more pages of said flash memory system to anon-binary log likelihood ratio based on a probability that a given datapattern was written to said plurality of bits when a particular patternwas read from said plurality of bits; and a decoder for decoding saidplurality of bits using a binary decoder.